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LLMs substitute user identity for missing financial facts in investment advice

By PulseAugur Editorial · [1 sources] ·

A new paper explores how large language models like Llama-3.1-8B-Instruct substitute user identity for missing financial information when providing investment advice. Researchers found that when financial details were withheld, the model's recommendations shifted significantly based on the user's persona, with identity explaining a large portion of the variation in advice. The study also noted that the model sometimes invented financial details not provided in the prompt, particularly for larger households, and that gender was linearly decodable within the model's layers. AI

IMPACT Highlights potential biases in LLM financial advice and the need for auditing disclosure levels.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs substitute user identity for missing financial facts in investment advice

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The cluster contains an academic paper detailing research findings on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Saanvi Khetan, Sankar Balasubramanian ·

    Thin Evidence, Thick Priors: How Language Models Substitute Identity for Missing Financial Facts

    arXiv:2610.07798v1 Announce Type: new Abstract: People increasingly ask large language models what to do with their money, yet seldom describe their finances in full. This paper asks what a model does with the gap. Holding finances fixed and changing only who the investor is said…

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